Qiqi Xie

Background: How gene regulatory programs reorganize across space and time is central to development and disease, but current methods infer regulatory structure from single snapshots. The emergence of spatiotemporal transcriptomics calls for methods that resolve regulation along both axes. Results: We introduce SpaTemGRN, which jointly models observations from multi-timepoint slides in spatiotemporal transcriptomics to infer time-resolved spatial-unit-specific putative GRNs. Compared with existing methods, SpaTemGRN allows later-stage units to borrow statistical strength from spatially proximate and temporally preceding units. In the App ^NL−G−F mouse model of Alzheimer’s disease, SpaTemGRN revealed region- and age-dependent strengthening of complement–glia regulatory coupling. An early, broadly distributed complement signature precedes later, spatially focal coupling with astrocytic and microglial responses. In the developing mouse embryonic brain, SpaTemGRN identified progressively sharpening and spatially segregated regulatory programs as the early neural tube regionalizes. Across simulated datasets, SpaTemGRN recovered regulatory edges more robustly than four existing methods and maintained the most stable performance across stages. Conclusions: SpaTemGRN provides a flexible hypothesis-generating framework for investigating how spatially localized gene–gene dependencies are remodeled across biological stages. All source code for SpaTemGRN is available at https://github.com/yibingjiang/SpaTemGRN .

Histone H3 lysine 9 dimethylation and trimethylation (H3K9me2/3) are prevalent in human genomes, especially in heterochromatin and specific euchromatic genes. Methylation of H3K9 is modulated by enzymes such as SUV39H1, SUV39H2, SETDB1, SETDB2, and EHMT1/2, which influence cancer progression. This study reveals differential expression of these six H3K9 methyltransferases in tumors, with SUV39H1, SUV39H2, and SETDB1 showing significant links to cancer phenotypes. We developed the “H3K9me3 MtSig” (H3K9me3 methyltransferases signature) based on these findings. H3K9me3 MtSig is unique to various tumors, with prognostic significance and associations with key signaling pathways, especially in glioblastoma (GBM). Elevated H3K9me3 MtSig was observed in GBM samples, correlating with the G2/M cell cycle and reduced immune responses. H3K9me3-mediated repetitive sequence silencing by H3K9me3 MtSig contributed to these phenotypes, and inhibiting H3K9me3 MtSig in patient-derived GBM cells suppressed proliferation and increased immune responses. H3K9me3 MtSig serves as an independent prognostic factor and potential therapeutic target.

The aim of this study was to construct a new immune-associated long non-coding RNA (lncRNA) signature to predict the prognosis of Ewing sarcoma (ES) and explore its molecular mechanisms. We downloaded transcriptome and clinical prognosis data from the Gene Expression Omnibus (GSE17679, which included 88 ES samples and 18 matched normal skeletal muscle samples), and used it as a training set to identify immune-related lncRNAs with different expression levels in ES. Univariable Cox regression was used to screen immune-related lncRNAs related to ES prognosis, and an immune-related lncRNA signature was constructed based on machine learning iterative lasso regression. An external verification set was used to confirm the predictive ability of the signature. Clinical feature subgroup analysis was used to explore whether the signature was an independent prognostic factor. In addition, CIBERSORT was used to explore immune cell infiltration in the high- and low-risk groups, and to analyze the correlations between the lncRNA signature and immune cell levels. Gene set enrichment and variation analyses were used to explore the possible regulatory mechanisms of the immune-related lncRNAs in ES. We also analyzed the expression of 17 common immunotherapy targets in the high- and low-risk groups to identify any that may be regulated by immune-related lncRNAs. We screened 35 immune-related lncRNAs by univariate Cox regression. Based on this, an immune-related 11-lncRNA signature was generated by machine learning iterative lasso regression. Analysis of the external validation set confirmed its high predictive ability. DPP10 antisense RNA 3 was negatively correlated with resting dendritic cell, neutrophil, and γδ T cell infiltration, and long intergenic non-protein coding RNA 1398 was positively correlated with resting dendritic cells and M2 macrophages. These lncRNAs may affect ES prognosis by regulating GSE17721_CTRL_VS_PAM3CSK4_12H_BMDC_UP, GSE2770_IL4_ACT_VS_ACT_CD4_TCELL_48H_UP, GSE29615_CTRL_VS_DAY3_ LAIV_IFLU_VACCINE_PBMC_UP, complement signaling, interleukin 2-signal transducer and activator of transcription 5 signaling, and protein secretion. The immune-related 11-lncRNA signature may also have regulatory effects on the immunotherapy targets CD40 molecule, CD70 molecule, and CD276 molecule. In conclusion, we constructed a new immune-related 11-lncRNA signature that can stratify the prognoses of patients with ES.

Background: Osteosarcoma is a frequent bone malignancy in children and young adults. Despite the availability of some prognostic biomarkers, most of them fail to accurately predict prognosis in osteosarcoma patients. In this study, we used bioinformatics tools and machine learning algorithms to establish an autophagy-related long non-coding RNA (lncRNA) signature to predict the prognosis of osteosarcoma patients.Methods: We obtained expression and clinical data from osteosarcoma patients in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We acquired an autophagy gene list from the Human Autophagy Database (HADb) and identified autophagy-related lncRNAs by co-expression analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the autophagy-related lncRNAs were conducted. Univariate and multivariate Cox regression analyses were performed to assess the prognostic value of the autophagy-related lncRNA signature and validate the relationship between the signature and osteosarcoma patient survival in an independent cohort. We also investigated the relationship between the signature and immune cell infiltration.Results: We initially identified 69 autophagy-related lncRNAs, 13 of which were significant predictors of overall survival in osteosarcoma patients. Kaplan-Meier analyses revealed that the 13 autophagy-related lncRNAs could stratify patients based on their outcomes. Receiver operating characteristic curve analyses confirmed the superior prognostic value of the lncRNA signature compared to clinically used prognostic biomarkers. Importantly, the autophagy-related lncRNA signature predicted patient prognosis independently of clinicopathological characteristics. Furthermore, we found that the expression levels of the autophagy-related lncRNA signature were significantly associated with the infiltration levels of different immune cell subsets, including T cells, NK cells, and dendritic cells.Conclusion: The autophagy-related lncRNA signature established here is an independent and robust predictor of osteosarcoma patient survival. Our findings also suggest that the expression of these 13 autophagy-related lncRNAs may promote osteosarcoma progression by regulating immune cell infiltration in the tumor microenvironment.

Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p < 0.05, p < 0.01), and clinical subgroup showed that the signature’s prognostic ability was independent of clinicopathological factors. Univariate and multivariate Cox regression analyses also supported its prognostic value. In conclusion, we developed an IRGs signature that is a prognostic indicator in OS patients, and the signature might serve as potential prognostic indicator to identify outcome of OS and facilitate personalized management of the high-risk patients.

ObjectiveOsteosarcoma (OS), the most common malignant bone tumor, is prone to early metastasis and recurrence, resulting in poor prognosis. The purpose of this study was to investigate gene expression in OS samples, and to study the relationship between tumor infiltrating immune cells (TIICS) in the tumor microenvironment and the occurrence, development, and prognosis of OS. MethodsIntegrated analysis was performed on the gene expression profile of OS samples in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, and normal muscle tissue samples in the Genotype-Tissue Expression (GTEx) database. Screening of differentially expressed genes in OS samples and healthy muscle tissue samples was performed using R software. Functional annotation and pathway enrichment analyses were performed, a protein-protein interaction network was constructed, and the Hub genes were screened. The infiltration differences of 22 TIICs in OS samples and normal muscle tissue samples were analyzed using CIBERSORT. ROC curve, and OS survival analyses were performed on the Hub genes and 22 TIICs. The correlation between Hub genes with prognostic value and the 22 TIICs was discussed. ResultsTen Hub genes were screened. OS and normal muscle tissue samples were compared. Significant differences were observed in the infiltration of follicular helper T cells, resting NK cells, plasma cells, activated CD4 memory T cells, gamma delta T cells, monocytes, M0 macrophages and resting dendritic cells (P<0.05). ROC curve analysis showed that the area under the curve (AUC) for the 10 hub genes was 0.9 ~ 1.0, and for naive B cells (AUC=0.942), plasma cells (AUC=0.999), follicular T helper cells (AUC=0.814), resting NK cells (AUC=0.864), monocytes (AUC=0.987), and M0 macrophages (AUC=0.995). Kaplan-Meier curves revealed positive correlation between the expression levels of TNNI2, TTN, ACTN3, TNNC2, MYL3, and survival of OS patients. Correlation analysis revealed close association between ACTN3, TNNI2, TNNC2, MYL3, TTN, and the infiltration of M0 macrophages and plasma cells. ConclusionOur findings provide a theoretical basis for further study of the molecular mechanisms underlying OS development and occurrence. It serves as a reference for the development of new diagnostics, identification of prognostic biomarkers, and targeted drug therapy for OS.

Purpose Ewing sarcoma (ES) is one of the most common malignant bone tumors in children and adolescents. The immune microenvironment plays an important role in the development of ES. Here, we developed an optimal signature for determining ES patient prognosis based on immune-related genes (IRGs). Methods We analyzed the ES gene expression profile dataset, GSE17679, from the GEO database and extracted differential expressed IRGs (DEIRGs). Then, we conducted functional correlation and protein–protein interaction (PPI) analyses of the DEIRGs and used the machine learning algorithm-iterative Lasso Cox regression analysis to build an optimal DEIRG signature. In addition, we applied ES samples from the ICGC database to test the optimal gene signature. We performed univariate and multivariate Cox regressions on clinicopathological characteristics and optimal gene signature to evaluate whether signature is an important prognostic factor. Finally, we calculated the infiltration of 24 immune cells in ES using the ssGSEA algorithm, and analyzed the correlation between the DEIRGs in the optimal gene signature and immune cells. Results A total of 249 DEIRGs were screened and an 11-gene signature with the strongest correlation with patient prognoses was analyzed using a machine learning algorithm. The 11-gene signature also had a high prognostic value in the ES external verification set. Univariate and multivariate Cox regression analyses showed that 11-gene signature is an independent prognostic factor. We found that macrophages and cytotoxic, CD8 T, NK, mast, B, NK CD56bright, TEM, TCM, and Th2 cells were significantly related to patient prognoses; the infiltration of cytotoxic and CD8 T cells in ES was significantly different. By correlating prognostic biomarkers with immune cell infiltration, we found that FABP4 and macrophages, and NDRG1 and Th2 cells had the strongest correlation. Conclusion Overall, the IRG-related 11-gene signature can be used as a reliable ES prognostic biomarker and can provide guidance for personalized ES therapy.

Background: Osteosarcoma (OS) is a bone malignant tumor that occurs in children and adolescents. Due to a lack of reliable prognostic biomarkers, the prognosis of OS patients is often uncertain. This study aimed to construct an autophagy-related gene signature to predict the prognosis of OS patients.Methods: The gene expression profile data of OS and normal muscle tissue samples were downloaded separately from the Therapeutically Applied Research To Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases . The differentially expressed autophagy-related genes (DEARGs) in OS and normal muscle tissue samples were screened using R software, before being subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A protein-protein interaction (PPI) network was constructed and hub autophagy-related genes were screened. Finally, the screened autophagy-related genes were subjected to univariate Cox regression, Lasso Cox regression, survival analysis, and clinical correlation analysis.Results: A total of 120 DEARGs and 10 hub autophagy-related genes were obtained. A prognostic autophagy-related gene signature consisting of 9 genes ( BNIP3 , MYC , BAG1 , CALCOCO2 , ATF4 , AMBRA1 , EGFR , MAPK1 , and PEX ) was constructed. This signature was significantly correlated to the prognosis ( P <0.0001) and distant metastasis of OS patients ( P = 0.013).Conclusion: This signature based on 9 autophagy-related genes could predict metastasis and survival in patients with OS.